{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/can-peripheral-representations-improve","title":"Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?","arxiv_id":"1608.04042","date":"2016-08-14","proceeding":"NeurIPS 2016 12","authors":["Arturo Deza","Miguel P. Eckstein"],"abstract":"Previous studies have proposed image-based clutter measures that correlate\nwith human search times and/or eye movements. However, most models do not take\ninto account the fact that the effects of clutter interact with the foveated\nnature of the human visual system: visual clutter further from the fovea has an\nincreasing detrimental influence on perception. Here, we introduce a new\nfoveated clutter model to predict the detrimental effects in target search\nutilizing a forced fixation search task. We use Feature Congestion (Rosenholtz\net al.) as our non foveated clutter model, and we stack a peripheral\narchitecture on top of Feature Congestion for our foveated model. We introduce\nthe Peripheral Integration Feature Congestion (PIFC) coefficient, as a\nfundamental ingredient of our model that modulates clutter as a non-linear gain\ncontingent on eccentricity. We finally show that Foveated Feature Congestion\n(FFC) clutter scores r(44) = -0.82 correlate better with target detection (hit\nrate) than regular Feature Congestion r(44) = -0.19 in forced fixation search.\nThus, our model allows us to enrich clutter perception research by computing\nfixation specific clutter maps. A toolbox for creating peripheral\narchitectures: Piranhas: Peripheral Architectures for Natural, Hybrid and\nArtificial Systems will be made available.","url_abs":"http://arxiv.org/abs/1608.04042v1","url_pdf":"http://arxiv.org/pdf/1608.04042v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"can-peripheral-representations-improve","repo_url":"https://github.com/ArturoDeza/Piranhas","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.04042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1608.04042"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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